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In our policy paper we used the confidence interval for a proportion to test significance ( Z-test)

Correlation

Calculated correlation coefficient( r) between the pairs of variables (time spent on phone, time spent on internet, computer proficiency, previous background in statistics) for male and female sub-samples

Run two regressions for each of the sub-samples( male and female) in order to find out if there could be any linear relationship between pairs of the variables (time spent on phone, time spent on internet, computer proficiency, previous background in statistics), putting the particular stress on significance of the model and variables F and P values

Compare the results of the relevant regressions between the genders

Running outlier test to identify outliers

Excluding the outliers from the data, make the comparison of the two models with and without outliers for both subsamples

Analysis made on the basis of the unique sample, encompassing representatives of over 40 nationalities, very diverse academic and professional backgrounds. ( Normally the analysis made either on the basis of more homogenous or bigger number/size of samples)

What we learnt:

Fundamental importance of survey structure

The crucial need to determine the most appropriate technique to the particular data analysis

How to balance between narrative and technically analytical part of PP

Policy Implications:

Males should be encouraged to apply to the MDev

There should be more courses on poverty in the MDev structure (currently, there is only one offered by the institute)